Machines learn to connect images and text by training on large datasets, where more data helps models recognize patterns and improve accuracy. Vision-language models (VLMs) rely on these datasets to perform tasks like image captioning and visual question answering. The question is whether increasing datasets to 100 billion examples dramatically improves accuracy, cultural diversity, and…
The dominant approach to pretraining large language models (LLMs) relies on next-token prediction, which has proven effective in capturing linguistic patterns. However, this method comes with notable limitations. Language tokens often convey surface-level information, requiring models to process vast amounts of data to develop deeper reasoning capabilities. Additionally, token-based learning struggles with capturing long-term dependencies,…
Artificial Intelligence is increasingly integrated into various sectors, yet there is limited empirical evidence on its real-world application across industries. Traditional research methods—such as predictive modeling and user surveys—struggle to capture AI’s evolving role in workplaces. This makes it difficult to assess its influence on productivity, labor markets, and economic structures. A more data-driven approach…
Test-Time Scaling (TTS) is a crucial technique for enhancing the performance of LLMs by leveraging additional computational resources during inference. Despite its potential, there has been little systematic analysis of how policy models, Process Reward Models (PRMs), and problem complexity influence TTS, limiting its practical application. TTS can be categorized into Internal TTS, which encourages…
Artificial intelligence models face a fundamental challenge in efficiently scaling their reasoning capabilities at test time. While increasing model size often leads to performance gains, it also demands significant computational resources and extensive training data, making such approaches impractical for many applications. Traditional techniques, such as expanding model parameters or employing Chain-of-Thought (CoT) reasoning, rely…
Artificial intelligence has made significant strides, yet developing models capable of nuanced reasoning remains a challenge. Many existing models struggle with complex problem-solving tasks, particularly in mathematics, coding, and scientific reasoning. These difficulties often arise due to limitations in data quality, model architecture, and the scalability of training processes. The need for open-data reasoning models…
Reasoning tasks are yet a big challenge for most of the language models. Instilling a reasoning aptitude in models, particularly for programming and mathematical applications that require solid sequential reasoning, seems far distant. This problem could be attributed to the inherent complexity of these tasks that require a multi-step logical deduction approach planned with domain…